class_docstr: The type of the wrapped model returned from [`DdpAccelerator.wrap_model`](#wrap_model).
code: def clip_grad_norm_(
      self, max_norm: Union[float, int]
  ) -> torch.Tensor:
      return clip_grad_norm_(
          [p for p in self.model.parameters() if p.grad is not None],
          max_norm,
      )
  
imports: import os
  from typing import Union, Tuple, OrderedDict, Dict, NamedTuple, List, Optional, Any, Sequence, TYPE_CHECKING
  import torch
  import torch.distributed
  from torch.cuda import amp
  from torch.nn.utils import clip_grad_norm_
  from allennlp.common import Registrable
  from allennlp.common.util import int_to_device
  from allennlp.nn.parallel.sharded_module_mixin import ShardedModuleMixin
label_desc: Use image node_id 6 for calling the DdpWrappedModel obj's underlying member method code with example usage: obj.clip_grad_norm_(max_norm) and returns: clip_grad_norm_